Workplace accommodations for persons with physical disabilities: evidence synthesis of the peer-reviewed literature
Bibliographic record
Abstract
PURPOSE: To identify and synthesize research evidence on workplace accommodations used by employers to recruit, hire, retain, and promote persons with physical disabilities. METHOD: A structured search of six electronic journal databases was undertaken to identify peer-reviewed literature on the topic published from January 1990 to March 2016. Articles describing or evaluating workplace disability accommodation policies and practices were given a full-text review. Topic experts were contacted to identify additional studies. RESULTS: Details on specific accommodations described in 117 articles were synthesized and organized into three groups comprised of a total of 12 categories. The majority of studies did not rigorously evaluate effectiveness or cost-effectiveness of the accommodations under study. CONCLUSIONS: This evidence synthesis provides an overview of the peer-reviewed literature of value to occupational rehabilitation professionals and employers seeking guidance on workplace accommodation policies and practices for persons with physical disabilities. A wide range of accommodation options is available for addressing physical, social, and attitudinal barriers to successful employment. Besides physical/technological modifications, accommodations to enhance workplace flexibility and worker autonomy and strategies to promote workplace inclusion and integration are important. More comprehensive reporting and evaluations of the effectiveness of accommodations in research literature are needed to develop best practices for accommodating persons with disabilities. Implications for rehabilitation There is a substantial peer-reviewed literature that provides insights into the barriers for persons with physical disabilities and the workplace accommodation practices to address them, though rigorous evaluations of effectiveness and cost-effectiveness are uncommon. Attitudinal and social barriers stemming from stereotypes, ignorance and lack of knowledge are as important as physical barriers to employment for persons with physical disabilities. In addition to physical/technological modifications, accommodations to enhance workplace flexibility and autonomy of a worker, as well as strategies to promote workplace inclusion and integration may facilitate successful employment of persons with physical disabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".